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Structural Mixtures for Statistical Layout Analysis
| Citations: | 4 - 3 self |
BibTeX
@MISC{Shafait_structuralmixtures,
author = {Faisal Shafait and Joost Van Beusekom and Daniel Keysers and Thomas M. Breuel},
title = {Structural Mixtures for Statistical Layout Analysis},
year = {}
}
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Abstract
A key limitation of current layout analysis methods is that they rely on many hard-coded assumptions about document layouts and can not adapt to new layouts for which the underlying assumptions are not satisfied. Another major drawback of these approaches is that they do not return confidence scores for their outputs. These problems pose major challenges in large scale digitization efforts where a large number of different layouts need to be handled and manual inspection of the results on each individual page is not feasible. This paper presents a novel statistical approach to layout analysis that aims at solving the abovementioned problems for Manhattan layouts. The presented approach models known page layouts as a structural mixture model. A probabilistic matching algorithm is presented
Keyphrases
structural mixture statistical layout analysis many hard-coded assumption document layout different layout manual inspection current layout analysis method probabilistic matching algorithm manhattan layout key limitation major drawback large scale digitization effort underlying assumption large number new layout individual page structural mixture model major challenge presented approach model confidence score novel statistical approach
